三维点云分类和语义分割的深度学习技术综述

IF 2.4 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Machine Vision and Applications Pub Date : 2024-05-18 DOI:10.1007/s00138-024-01543-1

Sushmita Sarker, Prithul Sarker, Gunner Stone, Ryan Gorman, Alireza Tavakkoli, George Bebis, Javad Sattarvand

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引用次数: 0

摘要

点云分析在计算机视觉、机器人操纵和自动驾驶等许多领域有着广泛的应用。虽然深度学习在基于图像的任务中取得了显著成就，但深度神经网络在处理大量、无序、不规则和嘈杂的三维点时面临着许多独特的挑战。为了激励未来的研究，本文分析了点云处理所采用的深度学习方法的最新进展，并提出了推进这一领域的挑战和潜在方向。本文全面回顾了三维点云处理中的两大任务，即三维形状分类和语义分割。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation

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A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation

Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unordered, irregular and noisy 3D points. To stimulate future research, this paper analyzes recent progress in deep learning methods employed for point cloud processing and presents challenges and potential directions to advance this field. It serves as a comprehensive review on two major tasks in 3D point cloud processing—namely, 3D shape classification and semantic segmentation.

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来源期刊

Machine Vision and Applications 工程技术-工程：电子与电气

CiteScore

6.30

自引率

3.00%

发文量

审稿时长

8.7 months

期刊介绍： Machine Vision and Applications publishes high-quality technical contributions in machine vision research and development. Specifically, the editors encourage submittals in all applications and engineering aspects of image-related computing. In particular, original contributions dealing with scientific, commercial, industrial, military, and biomedical applications of machine vision, are all within the scope of the journal. Particular emphasis is placed on engineering and technology aspects of image processing and computer vision. The following aspects of machine vision applications are of interest: algorithms, architectures, VLSI implementations, AI techniques and expert systems for machine vision, front-end sensing, multidimensional and multisensor machine vision, real-time techniques, image databases, virtual reality and visualization. Papers must include a significant experimental validation component.